scholarly journals Rapid Clinical Screening and Staging for COVID-19 Severe Outcome - A Hospitalization Study in New York City

2021 ◽  
Author(s):  
Chaorui C Huang
Keyword(s):  
Risk Factors ◽  
New York ◽  
Logistic Regression ◽  
Diagnostic Accuracy ◽  
Roc Curve ◽  
Fatal Outcome ◽  
Demographic Variables ◽  
Hospitalized Patients ◽  
Hospital Death ◽  
Morbid Conditions

Background: We aimed to evaluate the risk factors for Coronavirus disease 2019 (COVID-19) related severe outcome (in-hospital death) among the hospitalized patients in New York State (NYS) and proposed a method that could be used to inform future work to develop clinical algorithms and predict resource needs for hospitalized COVID-19 patients. Methods: We analyzed covid-19 related hospitalization in NYS from April 1st to November 17th, 2020, using Statewide Planning and Research Cooperative System (SPARCS) discharge dataset. Logistic regression was performed to evaluate the risk factors for COVID-19 related in-hospital death using demographic variables, symptom, rapid clinical examination, and medical history of chronic co-morbid conditions. Receiver operating characteristic (ROC) curve was calculated, and cut-off points for predictors were selected to stage the risk of COVID-19 related fatal outcome. Results: Logistic regression analysis showed age was the greatest risk factor for COVID-19 related fatal outcome among the hospitalized patients, which by itself achieved the diagnostic accuracy of 0.78 represented by the area under the ROC curve. By adding other demographic variables, dyspnea or hypoxemia and multiple chronic co-morbid conditions, the diagnostic accuracy was improved to 0.85. We selected cut-off points for predictors and provided a general recommendation to categorize the levels of risk for COVID-19 related fatal outcome. Conclusions: We assessed risk factors associated with in-hospital COVID-19 mortality and identified cut-off points that might be used to categorize the level of risk. Further studies are warranted to evaluate laboratory tests and develop laboratory biomarkers to improve the diagnostic accuracy for early intervention.

Clinical and Demographic Risk Factors for COVID-19 during Delivery Hospitalizations in New York City

2021 ◽  
Author(s):  
Desmond Sutton ◽  
Timothy Wen ◽  
Anna P. Staniczenko ◽  
Yongmei Huang ◽  
Maria Andrikopoulou ◽  
...  
Keyword(s):  
Risk Factors ◽  
New York ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Demographic Data ◽  
Outcome Data ◽  
Demographic Risk ◽  
Demographic Risk Factors ◽  
Zip Code

Objective This study was aimed to review 4 weeks of universal novel coronavirus disease 2019 (COVID-19) screening among delivery hospitalizations, at two hospitals in March and April 2020 in New York City, to compare outcomes between patients based on COVID-19 status and to determine whether demographic risk factors and symptoms predicted screening positive for COVID-19. Study Design This retrospective cohort study evaluated all patients admitted for delivery from March 22 to April 18, 2020, at two New York City hospitals. Obstetrical and neonatal outcomes were collected. The relationship between COVID-19 and demographic, clinical, and maternal and neonatal outcome data was evaluated. Demographic data included the number of COVID-19 cases ascertained by ZIP code of residence. Adjusted logistic regression models were performed to determine predictability of demographic risk factors for COVID-19. Results Of 454 women delivered, 79 (17%) had COVID-19. Of those, 27.9% (n = 22) had symptoms such as cough (13.9%), fever (10.1%), chest pain (5.1%), and myalgia (5.1%). While women with COVID-19 were more likely to live in the ZIP codes quartile with the most cases (47 vs. 41%) and less likely to live in the ZIP code quartile with the fewest cases (6 vs. 14%), these comparisons were not statistically significant (p = 0.18). Women with COVID-19 were less likely to have a vaginal delivery (55.2 vs. 51.9%, p = 0.04) and had a significantly longer postpartum length of stay with cesarean (2.00 vs. 2.67days, p < 0.01). COVID-19 was associated with higher risk for diagnoses of chorioamnionitis and pneumonia and fevers without a focal diagnosis. In adjusted analyses, including demographic factors, logistic regression demonstrated a c-statistic of 0.71 (95% confidence interval [CI]: 0.69, 0.80). Conclusion COVID-19 symptoms were present in a minority of COVID-19-positive women admitted for delivery. Significant differences in obstetrical outcomes were found. While demographic risk factors demonstrated acceptable discrimination, risk prediction does not capture a significant portion of COVID-19-positive patients. Key Points

Incidence and risk factors for community-associated methicillin-resistant Staphylococcus aureus in New York City, 2006–2012

2015 ◽  
Vol 144 (5) ◽  
pp. 1014-1017 ◽  
Author(s):  
P. BAKER ◽  
B. COHEN ◽  
J. LIU ◽  
E. LARSON
Keyword(s):  
Risk Factors ◽  
New York ◽  
Staphylococcus Aureus ◽  
New York City ◽  
Logistic Regression ◽  
York City ◽  
Methicillin Resistant Staphylococcus Aureus ◽  
Methicillin Resistant ◽  
Factors Associated ◽  
History Of

SUMMARYThis study aims to describe changes in incidence and risk factors for community-associated methicillin resistant Staphylococcus aureus (CA-MRSA) infections upon admission to two New York City hospitals from 2006 to 2012. We examined the first hospitalization for adult patients using electronic health record and administrative data and determined the annual incidence/1000 admissions of total S. aureus, total MRSA, and CA-MRSA (within 48 h of admission) in clinical specimens over the study period. Logistic regression was used to identify factors associated with CA-MRSA in 2006 and 2012. In 137 350 admissions, the incidence of S. aureus, MRSA, and CA-MRSA/1000 admissions were 15·6, 7·0, and 3·5, respectively. The total S. aureus and MRSA isolations decreased significantly over the study period (27% and 25%, respectively) while CA-MRSA incidence was unchanged. CA-MRSA increased as a proportion of all MRSA between 2006 (46%) and 2012 (62%), and was most frequently isolated from respiratory (1·5/1000) and blood (0·7/1000) cultures. Logistic regression analysis of factors associated with isolation of CA-MRSA showed that age ⩾65 years [odds ratio (OR) 2·3, 95% confidence interval (CI) 1·2–4·5], male gender (OR 1·8, 95% CI 1·2–2·8) and history of renal failure (OR 2·6, 95% CI 1·6–4·2) were significant predictors of infection in 2006. No predictors were identified in 2012.

scholarly journals Epidemic Diphtheria in the Republic of Georgia, 1993–1996: Risk Factors for Fatal Outcome among Hospitalized Patients

2000 ◽  
Vol 181 (s1) ◽  
pp. S130-S137 ◽  
Author(s):  
M. Linda Quick ◽  
Roland W. Sutter ◽  
Ketevan Kobaidze ◽  
Naile Malakmadze ◽  
Peter M. Strebel ◽  
...  
Keyword(s):  
Risk Factors ◽  
Fatal Outcome ◽  
Hospitalized Patients ◽  
Republic Of Georgia ◽  
The Republic

scholarly journals Title: Preoperative albumin-to-fibrinogen ratio predicts postoperative pulmonary complications in patients after minimally invasive esophagectomy

2021 ◽  
Author(s):  
Zhang Peng ◽  
Zhao Song
Keyword(s):  
Risk Factors ◽  
Esophageal Cancer ◽  
Logistic Regression ◽  
Roc Curve ◽  
Training Group ◽  
Pulmonary Complications ◽  
Curve Analysis ◽  
Postoperative Pulmonary Complications ◽  
Multivariate Logistic Regression ◽  
Roc Curve Analysis

Abstract Background Postoperative pulmonary complications (PPCs) are the most common postoperative complications in patients with esophageal cancer. Prediction of PPCs by establishing a preoperative physiological function parameter model can help patients make adequate preoperative preparation, reduce treatment costs, and improve prognosis and quality of life. The purpose of this study was to investigate the relationship between albumin-to-fibrinogen ratio (AFR), prognostic nutritional index (PNI), albumin-to-globulin ratio (AGR), neutrophils-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), and monocyte-to -lymphocyte ratio (MLR) and other preoperative laboratory tests and PPCs in patients after esophagectomy. Methods Retrospective analysis was performed on total 712 consecutive patients who underwent esophagectomy in the Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University from July 2018 to December 2020. Patients were divided into training (535 patients) and validation (177) groups for comparison of baseline data, perioperative indicators, and laboratory examination data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy, sensitivity and specificity of AFR, and Youden’s index was used to calculate the cut-off values of AFR. Univariate and multivariate logistic regression analyses were used to assess the risk factors for PPCs in training group. Results 112 (20.9%) in training group and 36 (20.3%) in validation group developed PPCs. The AUC value predicted by AFR using ROC curve analysis was 0.817, sensitivity 76.2% and specificity 78.7% in training group while AUC 0.803, sensitivity 69.4% and specificity 85.8%. Multivariate logistic regression analysis showed that smoking index, American Society of Anesthesiologists (ASA), AFR, and recurrent laryngeal nerve palsy were independent risk factors for PPCs. Conclusion Preoperative AFR can effectively predict the occurrence of PPCs in patients with esophageal cancer

scholarly journals Risk-Predictors of Mortality in Hospitalized Patients with Thrombotic Thrombocytopenic Purpura: Nationally Representative Data from 2007-2011

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4290-4290
Author(s):  
Ruchika Goel ◽  
Paul Ness ◽  
Clifford M. Takemoto ◽  
Karen E. King ◽  
Aaron Tobian
Keyword(s):  
Risk Factors ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Thrombotic Thrombocytopenic Purpura ◽  
Arterial Thrombosis ◽  
Hospitalized Patients ◽  
Thrombocytopenic Purpura ◽  
Plasma Infusion ◽  
Multivariable Logistic Regression

Abstract Introduction: Survivors of Thrombotic Thrombocytopenic Purpura (TTP) hospitalizations have been proposed to be at higher risk for long term poor clinical outcomes and premature death. Patients with TTP have a high risk for in-hospital morbidity and mortality as well. However, there is a paucity of data on the predictors of adverse outcomes including death in hospitalized patients with TTP. Methods: A weighted analysis of 5 years (2007-2011) using data from the Nationwide Inpatient Sample, a stratified probability sample of 20% of all hospital discharges among community hospitals in the United States (approximately 1100 hospitals/year), was performed. Hospitalizations with TTP as the primary admitting diagnoses were identified using the ICD-9 discharge code 446.6. Univariate and stepwise multivariable logistic regression analyses with elimination were used for statistical analysis. Based on results of univariate analysis, the significant variables were added in a stepwise manner in a multivariable model. All variables selected for the multivariable model were tested for interaction with a significance threshold level of p<0.2. Except for this, all hypothesis testing was two tailed and p<0.05 was considered significant. Receiver Operator Characteristics (ROC) curve was constructed using risk factors on multivariate analysis. Results: The all-cause mortality rate was 8.7% (918/10615) among admissions with primary diagnosis of TTP (0.5% pediatric, 65.9% female, 58.2% Caucasian, 27.2% African-American). Table 1 lists the risk factors by univariate analysis and includes a) factors with significantly higher odds of mortality and b) other putative factors which were not statistically significant predictors. Table 2: In stepwise multivariable logistic regression analysis: arterial thrombosis (adjOR 5.1 95%CI=1.1-31.7), acute myocardial infarction (adjOR 2.8, 95%CI=1.6-4.9), non-occurrence of either intervention: plasmapheresis or fresh frozen plasma infusion (adjOR 2.0, 95% CI=1.4-2.9) 4) requirement of platelet transfusions during hospitalization (adjOR 2.0, 95%CI= 1.3-3.2) and every ten year increase in age (OR 1.4 95%CI=1.3-1.6) were independently predictive of mortality in TTP patients (area under the curve for ROC 74%, Figure 1). Conclusion: We present a set of independent risk factors that may potentially be used in a predictive model of mortality in TTP. Early and targeted aggressive therapy based on these factors should guide the management of hospitalized patients with TTP for improved outcomes. Table 1.Unadjusted odds of in-hospital mortality.Significant predictors of mortality for TTP on univariate analysisOdds Ratio95% Confidence LimitsArterial Thrombosis 10.92.254.6AMI 3.72.16.2STROKE 4.93.07.9Platelet Transfusion 2.31.53.6Bleeding event 1.71.12.6Plasmapheresis (No vs. Yes)1.61.22.3plasmapheresis or plasma infusion (not performed)2.21.53.1Every 10 years increase in age1.51.31.6PRBC transfusion1.71.22.3Caucasian versus African American1.91.32.8Asian versus African American3.31.29.1V ariables not significant predictors of mortality for TTP on univariate analysis.Odds Ratio95% Confidence LimitsVenous Thrombosis/Thromboembolism1.90.84.4FEMALE versus male gender1.00.71.4Hypertension Yes vs. no0.90.61.2Diabetes Yes vs. no0.90.61.4Chronic Kidney Disease Yes vs. No1.40.92.2End Stage Renal Disease Yes vs. No0.90.41.9Overweight/Obese Yes vs. No0.70.41.5Variables meeting criteria for inclusion in multiple logistic regression model are in boldface type. Table 2. Multivariable Predictors for In Hospital Mortality in patients with primary diagnosis of TTP Adjusted Odds Ratio 95% Confidence Limits Arterial Thrombosis 6.0 1.2 30.5 Acute myocardial infarction 2.8 1.6 4.8 No Plasmapheresis/Plasma infusion 2.0 1.4 2.9 Platelet Transfusion 2.1 1.4 3.2 Age (per 10 year higher) 1.4 1.3 1.6 Female versus Male 1.2 0.8 1.7 TTP = Thrombotic Thrombocytopenic Purpura Step 0: Using arterial thrombosis Figure 1: Receiver- Operator-Characteristic Curve (ROC) overlay curve for the stepwise multivariable logistic regression risk prediction showing incremental AUC with addition of each risk factor for hospital patients with TTP. Figure 1:. Receiver- Operator-Characteristic Curve (ROC) overlay curve for the stepwise multivariable logistic regression risk prediction showing incremental AUC with addition of each risk factor for hospital patients with TTP. Step 1: Adding acute myocardial infarction Step 2: Adding plasmapheresis /fresh frozen plasma infusion Step 3: Adding platelet transfusions Final model: Adding every ten year increase in age. Disclosures Ness: Terumo BCT: Consultancy.

scholarly journals The Prediction Model of Risk Factors for COVID-19 Developing into Severe Illness Based on 1046 Patients with COVID-19

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhichuang Lian ◽  
Yafang Li ◽  
Wenyi Wang ◽  
Wei Ding ◽  
Zongxin Niu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Severe Illness ◽  
Roc Curve Analysis ◽  
Underlying Diseases

This study analyzed the risk factors for patients with COVID-19 developing severe illnesses and explored the value of applying the logistic model combined with ROC curve analysis to predict the risk of severe illnesses at COVID-19 patients’ admissions. The clinical data of 1046 COVID-19 patients admitted to a designated hospital in a certain city from July to September 2020 were retrospectively analyzed, the clinical characteristics of the patients were collected, and a multivariate unconditional logistic regression analysis was used to determine the risk factors for severe illnesses in COVID-19 patients during hospitalization. Based on the analysis results, a prediction model for severe conditions and the ROC curve were constructed, and the predictive value of the model was assessed. Logistic regression analysis showed that age (OR = 3.257, 95% CI 10.466–18.584), complications with chronic obstructive pulmonary disease (OR = 7.337, 95% CI 0.227–87.021), cough (OR = 5517, 95% CI 0.258–65.024), and venous thrombosis (OR = 7322, 95% CI 0.278–95.020) were risk factors for COVID-19 patients developing severe conditions during hospitalization. When complications were not taken into consideration, COVID-19 patients’ ages, number of diseases, and underlying diseases were risk factors influencing the development of severe illnesses. The ROC curve analysis results showed that the AUC that predicted the severity of COVID-19 patients at admission was 0.943, the optimal threshold was −3.24, and the specificity was 0.824, while the sensitivity was 0.827. The changes in the condition of severe COVID-19 patients are related to many factors such as age, clinical symptoms, and underlying diseases. This study has a certain value in predicting COVID-19 patients that develop from mild to severe conditions, and this prediction model is a useful tool in the quick prediction of the changes in patients’ conditions and providing early intervention for those with risk factors.

scholarly journals Clinical Characteristics on Admission Predict In-Hospital Fatal Outcome in Patients Aged ≥75 Years with novel coronavirus disease (COVID-19): A Retrospective Cohort Study

2020 ◽  
Author(s):  
Zhihua Yu ◽  
Yuhe Ke ◽  
Jiang Xie ◽  
Hao Yu ◽  
Wei Zhu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Temperature ◽  
Clinical Characteristics ◽  
Troponin I ◽  
Fatal Outcome ◽  
High Sensitivity ◽  
Lactic Dehydrogenase ◽  
Hospital Death ◽  
Laboratory Findings ◽  
Novel Coronavirus

Abstract Background：Novel coronavirus disease（COVID-19）has become a worldwide pandemic and precise fatality data by age group are needed urgently. This study to delineate the clinical characteristics and outcome of COVID-19 patients aged ≥75 years and identify the risk factors of in-hospital death.Methods：A total of 141 consecutive patients aged ≥75 years who were admitted to the hospital between 12th and 19th February 2020. In-hospital death, clinical characteristics and laboratory findings on admission were obtained from medical records. The final follow-up observation was 31st March 2020.Results：The median age was 81 years (84 female, 59.6%). Thirty-eight (27%) patients were classified as severe or critical cases. 18 (12.8%) patients had died in hospital and the remaining 123 were discharged. Patients who died were more likely to present with fever (38.9% vs. 7.3%); low percutaneous oxygen saturation(SpO2) (55.6% vs. 7.3%); reduced lymphocytes (72.2% vs. 35.8%) and platelets (27.8% vs. 4.1%); and increased D-dimer (94.4% vs. 42.3%), creatinine (50.0% vs. 22.0%), lactic dehydrogenase (LDH) (77.8% vs. 30.1%), high sensitivity troponin I (hs-TnI) (72.2% vs. 14.6%), and N-terminal pro-brain natriuretic peptide (NT-proBNP) (72.2% vs. 6.5%; all P<0.05) than patients who recovered. Male sex (odds ratio [OR]=13.1, 95% confidence interval[CI] 1.1 to 160.1, P=0.044), body temperature >37.3°C (OR=80.5, 95% CI 4.6 to 1407.6, P=0.003), SpO2≤90% (OR=70.1, 95% CI 4.6 to 1060.4, P=0.002), and NT-proBNP>1800ng/L (OR=273.5, 95% CI 14.7 to 5104.8, P<0.0001) were independent risk factors of in-hospital death. Conclusions：In-hospital fatality among COVID-19 patients can be estimated by sex and on-admission measurements of body temperature, SpO2, and NT-proBNP.

scholarly journals Association Between D-Dimer Level and In-Hospital Death of Pulmonary Embolism Patients

Dose-Response ◽  
2020 ◽  
Vol 18 (4) ◽  
pp. 155932582096843
Author(s):  
Zi-Kai Song ◽  
Haidi Wu ◽  
Xiaoyan Xu ◽  
Hongyan Cao ◽  
Qi Wei ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Roc Curve ◽  
Curve Analysis ◽  
Hospital Death ◽  
Roc Curve Analysis ◽  
Survival Group ◽  
Death Group ◽  
D Dimer

To investigate whether D-dimer level could predict pulmonary embolism (PE) severity and in-hospital death, a total of 272 patients with PE were divided into a survival group (n = 249) and a death group (n = 23). Comparisons of patient characteristics between the 2 groups were performed using Mann-Whitney U test. Significant variables in univariate analysis were entered into multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was performed to determine the predictive value of D-dimer level alone or together with the simplified Pulmonary Embolism Severity Index (sPESI) for in-hospital death. Results showed that patients in the death group were significantly more likely to have hypotension (P = 0.008), tachycardia (P = 0.000), elevated D-dimer level (P = 0.003), and a higher sPESI (P = 0.002) than those in the survival group. Multivariable logistic regression analysis showed that D-dimer level was an independent predictor of in-hospital death (OR = 1.07; 95% CI, 1.003-1.143; P = 0.041). ROC curve analysis showed that when D-dimer level was 3.175 ng/ml, predicted death sensitivity and specificity were 0.913 and 0.357, respectively; and when combined with sPESI, specificity (0.838) and area under the curve (0.740) were increased. Thus, D-dimer level is associated with in-hospital death due to PE; and the combination with sPESI can improve the prediction level.

scholarly journals Predictive Factors of Clinical Response of Infliximab Therapy in Active Nonradiographic Axial Spondyloarthritis Patients

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Zhiming Lin ◽  
Zetao Liao ◽  
Jianlin Huang ◽  
Maixing Ai ◽  
Yunfeng Pan ◽  
...  
Keyword(s):  
New York ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Clinical Response ◽  
Predictive Factors ◽  
Roc Curve ◽  
Axial Spondyloarthritis ◽  
Infliximab Therapy ◽  
Infliximab Treatment ◽  
Nonradiographic Axial Spondyloarthritis

Objectives.To evaluate the efficiency and the predictive factors of clinical response of infliximab in active nonradiographic axial spondyloarthritis patients.Methods.Active nonradiographic patients fulfilling ESSG criteria for SpA but not fulfilling modified New York criteria were included. All patients received infliximab treatment for 24 weeks. The primary endpoint was ASAS20 response at weeks 12 and 24. The abilities of baseline parameters and response at week 2 to predict ASAS20 response at weeks 12 and 24 were assessed using ROC curve and logistic regression analysis, respectively.Results.Of 70 axial SpA patients included, the proportions of patients achieving an ASAS20 response at weeks 2, 6, 12, and 24 were 85.7%, 88.6%, 87.1%, and 84.3%, respectively. Baseline MRI sacroiliitis score (AUC = 0.791;P=0.005), CRP (AUC = 0.75;P=0.017), and ASDAS (AUC = 0.778,P=0.007) significantly predicted ASAS20 response at week 12. However, only ASDAS (AUC = 0.696,P=0.040) significantly predicted ASAS20 response at week 24. Achievement of ASAS20 response after the first infliximab infusion was a significant predictor of subsequent ASAS20 response at weeks 12 and 24 (waldχ2=6.87,P=0.009, and waldχ2=5.171,P=0.023).Conclusions.Infliximab shows efficiency in active nonradiographic axial spondyloarthritis patients. ASDAS score and first-dose response could help predicting clinical efficacy of infliximab therapy in these patients.

scholarly journals Risk Factors Determining the Outcome of 2-12 Months Age Group Infants Hospitalized With Severe Pneumonia

2013 ◽  
Vol 25 (1) ◽  
pp. 9-13 ◽  
Author(s):  
KMF Uddin ◽  
N Jahan ◽  
MA Manan ◽  
SA Ferdousi ◽  
T Farhana ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Fatal Outcome ◽  
Significant Risk ◽  
Multivariate Logistic Regression Analysis ◽  
Severe Pneumonia ◽  
World Health ◽  
Normal Infant ◽  
Age Group ◽  
Health Organization

Pneumonia is one of the leading causes of morbidity and mortality in under fives throughout the world, particularly in developing countries. A case control study was carried out in Bangabandhu Memorial Hospital, University of Science and Technology during the period of January to July 2006. 192 hospitalized infants of 2–12 months age group with World Health Organization(WHO) defined severe pneumonia with radiological confirmation were enrolled in the study, while controls were normal infant of same age group attending EPI center for vaccination. The children were managed using a standard protocol, factors were examined by univariate logistic regression analyasis. The factors whose odds ratio were significantly below 25% and considered as medically important were included in multivariate logistic regression analysis. Out of 192 children, 136(70.8%) were male, 56(29.2%) were female, 2-6 months old infants were 120(62.5%), >6 -12 months infants were 72(37.5%), malnutrition were present in 155( 80.72%), 145(75.5%) lived in slum area, 66(33.7%) were treated by quack, 63% were completely immunized and 3(10.5%) died. On multivariate analysis the following risk factors were found significant i.e. malnutrition, indoor smoke resulting from burning wood and manure used as fuel, non immunization, poor economy , poor housing. Significant risk factors for mortality in severe pneumonia are associated with 3rd degree malnutrition and congenital abnormality of heart with Downs syndrome. Malnutrition, indoor smoke, non-immunization, poor economy, poor housing, and smoking in bed room are important risk factors associated significantly with severe pneumonia and fatal outcome was associated with 3rd degree malnutrition. DOI: http://dx.doi.org/10.3329/medtoday.v25i1.15901 Medicine Today 2013 Vol.25(1): 9-13

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